Valorizing ‘Omics Visualization for Discovery

Tracking #: 457-1437

Authors:

Responsible editor:

Tobias Kuhn

Submission Type:

Position Paper

Abstract:

Scientists from diverse backgrounds are joining the field of data science. This leads to advances in data science being actualized in the context of many different domains. Conclusions from datasets using innovative algorithms are obvious aspects but advances in data science can take on many different forms such as new methods for data interpretation, new data integration and processing technologies, or as will be the topic of this editorial, data visualization techniques. The parity and complementary relationship between techniques from all domains provide ways to improve discovery although quantifying the contributions to discovery process from each technique can be elusive. The experiences described here come from a visualizing life science multi-omics data, but most of the remarks can be associated with visualization methods in general. From the perspective that visualization serves as an important method for shaping data science interpretations, this paper sets out some of the difficulties encountered in creating and valorizing new visualization implementations for scientific discovery from multi-omics datasets.

Date of Decision:

Decision:

Overall Impression: GoodSuggested Decision: AcceptTechnical Quality of the paper: GoodPresentation: GoodReviewer`s confidence: MediumSignificance: High significanceBackground: ReasonableNovelty: Limited noveltyData availability: All used and produced data are FAIR and openly available in established data repositoriesLength of the manuscript: The length of this manuscript is about right

Summary of paper in a few sentences (summary of changes and improvements for
second round reviews):

The paper defends the position that visualization techniques should be valued as important scientific artifacts in the field of data science, with a specific focus on the visualization of omics data. It does that by exploring, in general terms, what is a visualization, how visualizations are designed, what is their importance, and how they can help with the exploration of large amounts of complex scientific data.

Reasons to accept:

The reasons to accept stay mostly the same as my previous review, as I believe that the new revision did not do anything to change them. I reproduce them here to allow for a self-contained review: First, the role of the paper is not to introduce novel cutting-edge results from a specific research, but to defend a position, as described above (that visualizations are important scientific artifacts) and it does that well, without going too deep in specific problems and challenges. It is well supported by good references, it explores the subject in a broad yet objective manner, and succeeds to communicate its point. It is very well written and easy to read and understand.

Reasons to reject:

In my previous review I mentioned one disadvantage of the paper: while most of the text is developed with general visualization in mind, it mentions that the visualization of omics data is at its core, but very few concrete examples were described, and even those in very few details. I mentioned that, if possible, the author should have described one or two concrete cases of omics visualization in more details, in order to make the paper's position stronger. The author had the chance to write a new revision but chose not to improve this point. One thing that makes this point a bit more critical is that the title of the paper was changed to include the word "omics", making the paper's direction even more obvious, but still there is no concrete example of an omics visualization in the text. There are many references to works that deal with this, but a paragraph with one or two examples would have improved the paper considerably, in my opinion.

Overall Impression: AverageSuggested Decision: UndecidedTechnical Quality of the paper: WeakPresentation: AverageReviewer`s confidence: MediumSignificance: Moderate significanceBackground: Incomplete or inappropriateNovelty: Limited noveltyData availability: All used and produced data are FAIR and openly available in established data repositoriesLength of the manuscript: The length of this manuscript is about right

Summary of paper in a few sentences (summary of changes and improvements for
second round reviews):

The author improved the paper and added several elements to substantiate several arguments.
However, it still not well substantiated what position she takes. In 5 pages this paper talks about the role of visualizations, the role of the researcher when using visualizations, the role of design studies and the role of bias when doing research and ends with the discussion of building an interface. At the same time, it is also discusses the role of bias in research in general and how researchers deal with understanding their biases. There is hardly a logic structure and position in this paper. Too much aspects are pointed out while not creating a clear argued and specified paper. For example, the role of design studies seems to be come out of the blue without any clear reference why this is discussed. Also, while the role of bias when using visualizations could have been better argued and substantiated with more references, the author elaborates on bias in research in general which seems to lose focus on the subject of the paper.

Concluding, I am still not happy with the paper and hoped the author would have made a clearer argumented paper, taking one clear central argument to discuss and elaborate on

Reasons to accept:

no

Reasons to reject:

The author improved the paper and added several elements to substantiate several arguments.
However, it still not well substantiated what position she takes. In 5 pages this paper talks about the role of visualizations, the role of the researcher when using visualizations, the role of design studies and the role of bias when doing research and ends with the discussion of building an interface. At the same time, it is also discusses the role of bias in research in general and how researchers deal with understanding their biases. There is hardly a logic structure and position in this paper. Too much aspects are pointed out while not creating a clear argued and specified paper. For example, the role of design studies seems to come out of the blue without any clear reference why this is discussed. Also, while the role of bias when using visualizations could have been better argued and substantiated with more references, the author elaborates on bias in research in general which seems to lose focus on the subject of the paper.

Concluding, I am still not happy with the paper and hoped the author would have made a clearer argumented paper, taking one clear central argument to discuss and elaborate on

Overall Impression: GoodSuggested Decision: AcceptTechnical Quality of the paper: GoodPresentation: ExcellentReviewer`s confidence: HighSignificance: High significanceBackground: ReasonableNovelty: Limited noveltyData availability: All used and produced data are FAIR and openly available in established data repositoriesLength of the manuscript: The length of this manuscript is about right

Summary of paper in a few sentences (summary of changes and improvements for
second round reviews):

Not applicable

Reasons to accept:

Not applicable

Reasons to reject:

Not applicable

Further comments:

The revised version of this article reads very well. I only have one specific comment: "Node-link tree and graph visualizations, in both 2D and 3D [14], can display hierarchically structured data such as ontologies and networks." I would recommend to remove the reference to 3D graph visualization, as it is generally accepted that 3D visualization of graphs has little benefits and many drawbacks (occlusion, perspective foreshortening, the problem of lighting/shadows and color effects, navigation). Also, graphs are not (typically) hierarchical, only trees are. Here is a suggestion: "Node-link visualizations can display graphs/networks and trees, such as ontologies, protein interaction networks, or phylogenies."